{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 实战案例：基于交叉注意力的跨模态检索\n",
    "\n",
    "- 基于对应表示的模型VSE++建模了图文整体表示之间的关联\n",
    "- 基于交叉注意力的跨模态检索模型SCAN建模了图文更细粒度的局部表示之间的关联。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 模型训练流程\n",
    "\n",
    "![模型训练的一般流程](img/cr-traning_process.png)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 读取数据\n",
    "\n",
    "和VSE++相同，使用flickr8k作为实验数据集，需要先按照vse++的整理数据方式对数据进行整理。 \n",
    "- VSE++中已经介绍了其下载方式、划分方法。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### 提取图像区域表示\n",
    "\n",
    "数据集下载完成后，我们需要使用[bottom up attention模型](https://github.com/peteanderson80/bottom-up-attention)提取图像区域表示。具体而言，我们对每张图片提取36个检测框特征。安装配置好代码环境后，\n",
    "\n",
    "- 将脚本tools/generate_tsv.py 里的 MIN_BOXES 和 MAX_BOXES 值均设置为36，并在load_image_ids函数里增加flickr8k数据集的信息：\n",
    "```python\n",
    "elif split_name == 'flickr8k':\n",
    "    data_dir = '../data/flickr8k/'\n",
    "    with open(os.path.join(data_dir, 'dataset_flickr8k.json'), 'r') as j:\n",
    "        data = json.load(j)\n",
    "    for img in data['images']:\n",
    "        split.append((os.path.join(data_dir, 'images', img['filename']), img['imgid']))\n",
    "```\n",
    "- 使用下面的命令抽取图片表征（注意根据自己机器的配置更改gpu信息）：\n",
    "```\n",
    "./tools/generate_tsv.py --gpu 0,1,2,3 \\\n",
    "    --cfg  experiments/cfgs/faster_rcnn_end2end_resnet.yml \\\n",
    "    --def models/vg/ResNet-101/faster_rcnn_end2end_final/test.prototxt \\\n",
    "    --out resnet101_faster_rcnn_flickr8k.tsv \\\n",
    "    --net data/faster_rcnn_models/resnet101_faster_rcnn_final.caffemodel \\\n",
    "    --split flickr8k \n",
    "```\n",
    "- 调用脚本里的merge_tsvs函数，合并多个gpu的图像特征文件，并将结果拷贝至config的data_dir目录下。\n",
    "\n",
    "- 解析抽取的图像特征文件，将每张图片的36个检测框特征存储为单个npy格式文件，并将文件路径记录在数据json文件中。\n",
    "  - 为了后续的数据分析，将检测框的位置信息也以npy格式存储。\n",
    "  - json文件中的路径仅存储文件名前缀，加上后缀'.npy'为图像特征，加上后缀'.box.npy'为检测框特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import base64\n",
    "import csv\n",
    "import json\n",
    "import numpy as np\n",
    "import os\n",
    "import sys\n",
    "from PIL import Image\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "csv.field_size_limit(sys.maxsize)\n",
    "\n",
    "def resort_image_feature(dataset='flickr8k'):\n",
    "    karpathy_json_path = '../data/%s/dataset_flickr8k.json' % dataset\n",
    "    image_feature_path = '../data/%s/bottom_up_feature.tsv' % dataset\n",
    "    feature_folder = '../data/%s/image_box_features' % dataset\n",
    "    if not os.path.exists(feature_folder):\n",
    "        os.mkdir(feature_folder)\n",
    "\n",
    "    with open(karpathy_json_path, 'r') as j:\n",
    "        data = json.load(j)\n",
    "    img_id2filename = {img['imgid']:img['filename'] for img in data['images']}\n",
    "        \n",
    "    imgid2feature = {}\n",
    "    imgid2box = {}\n",
    "    FIELDNAMES = ['image_id', 'image_h', 'image_w', 'num_boxes', 'boxes', 'features']\n",
    "    with open(image_feature_path, 'r') as tsv_in_file:\n",
    "        reader = csv.DictReader(tsv_in_file, delimiter='\\t', fieldnames = FIELDNAMES)\n",
    "        for item in reader:\n",
    "            item['image_id'] = int(item['image_id'])\n",
    "            item['image_h'] = int(item['image_h'])\n",
    "            item['image_w'] = int(item['image_w'])\n",
    "            item['num_boxes'] = int(item['num_boxes'])\n",
    "            for field in ['boxes', 'features']:\n",
    "                buf = base64.b64decode(item[field])\n",
    "                temp = np.frombuffer(buf, dtype=np.float32)\n",
    "                item[field] = temp.reshape((item['num_boxes'],-1))\n",
    "            \n",
    "            imgid2feature[item['image_id']] = item['features']\n",
    "            imgid2box[item['image_id']] = item['boxes']\n",
    "            \n",
    "            np.save(os.path.join(feature_folder, img_id2filename[item['image_id']]+'.npy'), item['features'])\n",
    "            np.save(os.path.join(feature_folder, img_id2filename[item['image_id']]+'.box.npy'), item['boxes'])\n",
    "\n",
    "resort_image_feature()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "在调用该函数生成需要的格式的数据集文件之后，我们可以展示其中一条数据，简单验证下数据的格式是否和我们预想的一致。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<start> a black dog is looking through the fence <end>\n",
      "<start> a dark brown dog is running along a fence outside <end>\n",
      "<start> a brown dog runs along a fence <end>\n",
      "<start> the brown greyhound dog walks on green grass and looks through a fence <end>\n",
      "<start> a large black dog runs along a fence in the grass <end>\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 读取词典和验证集\n",
    "with open('../data/flickr8k/vocab.json', 'r') as f:\n",
    "    vocab = json.load(f)\n",
    "vocab_idx2word = {idx:word for word,idx in vocab.items()}\n",
    "with open('../data/flickr8k/val_data.json', 'r') as f:\n",
    "    data = json.load(f)\n",
    "\n",
    "# 展示第20张图片和36个区域，其对应的文本描述序号是100到104\n",
    "content_img = Image.open(data['IMAGES'][20])\n",
    "for i in range(5):\n",
    "    print(' '.join([vocab_idx2word[word_idx] for word_idx in data['CAPTIONS'][20*5+i]]))\n",
    "\n",
    "fig = plt.imshow(content_img)\n",
    "feats = np.load(data['IMAGES'][20].replace('images','image_box_features')+'.box.npy')\n",
    "for i in range(feats.shape[0]):\n",
    "    bbox = feats[i,:]\n",
    "    color = 'red'\n",
    "    if i > 3:\n",
    "        color = 'blue'\n",
    "    fig.axes.add_patch(plt.Rectangle(\n",
    "        xy=(bbox[0], bbox[1]), width=bbox[2]-bbox[0], height=bbox[3]-bbox[1],\n",
    "        fill=False, edgecolor=color, linewidth=1))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### 定义数据集类\n",
    "\n",
    "按照惯例，在准备好的数据集的基础上，我们需要进一步定义PyTorch Dataset类，以使用PyTorch DataLoader类按批次产生数据。\n",
    "- 具体方法还是继承torch.utils.data.Dataset类，并实现__getitem__和__len__两个函数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from argparse import Namespace \n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\n",
    "from torch.utils.data import Dataset\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "class ImageBoxTextDataset(Dataset):\n",
    "    \"\"\"\n",
    "    PyTorch数据类，用于PyTorch DataLoader来按批次产生数据\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, dataset_path, vocab_path, split, captions_per_image=5, max_len=30):\n",
    "        \"\"\"\n",
    "        参数：\n",
    "            dataset_path：json格式数据文件路径\n",
    "            vocab_path：json格式词典文件路径\n",
    "            split：train、val、test\n",
    "            captions_per_image：每张图片对应的文本描述数\n",
    "            max_len：文本描述包含的最大单词数\n",
    "        \"\"\"\n",
    "        self.split = split\n",
    "        assert self.split in {'train', 'val', 'test'}\n",
    "        self.cpi = captions_per_image\n",
    "        self.max_len = max_len\n",
    "        # 载入数据集\n",
    "        with open(dataset_path, 'r') as f:\n",
    "            self.data = json.load(f)\n",
    "        # 载入词典\n",
    "        with open(vocab_path, 'r') as f:\n",
    "            self.vocab = json.load(f)\n",
    "\n",
    "        # Total number of datapoints\n",
    "        self.dataset_size = len(self.data['CAPTIONS'])\n",
    "\n",
    "    def __getitem__(self, i):\n",
    "        # 第i个文本描述对应第(i // captions_per_image)张图片\n",
    "        img = torch.Tensor(np.load(self.data['IMAGES'][i // self.cpi].replace('images','image_box_features')+'.npy'))\n",
    "        caplen = len(self.data['CAPTIONS'][i])\n",
    "        caption = torch.LongTensor(self.data['CAPTIONS'][i]+ [self.vocab['<pad>']] * (self.max_len + 2 - caplen))\n",
    "\n",
    "        return img, caption, caplen\n",
    "        \n",
    "\n",
    "    def __len__(self):\n",
    "        return self.dataset_size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### 批量读取数据\n",
    "\n",
    "利用刚才构造的数据集类，借助DataLoader类构建能够按批次产生训练、验证和测试数据的对象。\n",
    "- 这里由于图像表示是预先提取的，因此不再需要对图像数据进行增强操作。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mktrainval(data_dir, vocab_path, batch_size, workers=4):    \n",
    "    train_set = ImageBoxTextDataset(os.path.join(data_dir, 'train_data.json'), \n",
    "                                    vocab_path, 'train')\n",
    "    valid_set = ImageBoxTextDataset(os.path.join(data_dir, 'val_data.json'), \n",
    "                                    vocab_path, 'val')\n",
    "    test_set = ImageBoxTextDataset(os.path.join(data_dir, 'test_data.json'), \n",
    "                                   vocab_path, 'test')\n",
    "\n",
    "    train_loader = torch.utils.data.DataLoader(\n",
    "        train_set, batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True)\n",
    "\n",
    "    valid_loader = torch.utils.data.DataLoader(\n",
    "        valid_set, batch_size=batch_size, shuffle=False, num_workers=workers, pin_memory=True, drop_last=False)\n",
    "    \n",
    "    test_loader = torch.utils.data.DataLoader(\n",
    "        test_set, batch_size=batch_size, shuffle=False, num_workers=workers, pin_memory=True, drop_last=False)\n",
    "\n",
    "    return train_loader, valid_loader, test_loader    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 定义模型\n",
    "\n",
    "SCAN模型是由图像表示提取器和文本表示提取器构成，二者提取图像中的每个区域和文本中的每个词的表示。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### 图像表示提取器\n",
    "\n",
    "在图像特征基础上增加一个全连接层，以输出符合对应表示空间维度的图像编码。\n",
    "- 图像区域输入特征的形状为(batch_size, 36, 2048)。由于PyTorch中的全连接层实现nn.Linear默认对最后一个维度执行变换，因此，这里并不需要对输入特征的形状进行特殊的处理。\n",
    "- 需要注意的是，这里也对图像表示进行了长度归一化，归一化时需要指定维度。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ImageRepExtractor(nn.Module):\n",
    "    def __init__(self, img_feat_dim, embed_size):\n",
    "        super(ImageRepExtractor, self).__init__()\n",
    "        self.fc = nn.Linear(img_feat_dim, embed_size)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        out = self.fc(x)\n",
    "        out = nn.functional.normalize(out, dim=-1)\n",
    "        return out\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### 文本表示提取器\n",
    "\n",
    "SCAN模型使用双向GRU模型作为文本表示提取器，它的输入层为词嵌入形式，词的表示为其对应的前向和后向的最后一个隐藏层输出的平均值。\n",
    "- 词表示的维度也进行了长度归一化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TextRepExtractor(nn.Module):\n",
    "    def __init__(self, vocab_size, word_dim, embed_size, num_layers):\n",
    "        super(TextRepExtractor, self).__init__()\n",
    "        self.embed_size = embed_size\n",
    "        self.embed = nn.Embedding(vocab_size, word_dim)\n",
    "        self.rnn = nn.GRU(word_dim, embed_size, num_layers, batch_first=True, bidirectional=True)\n",
    "\n",
    "        self.init_weights()\n",
    "    \n",
    "    def init_weights(self):\n",
    "        self.embed.weight.data.uniform_(-0.1, 0.1)\n",
    "\n",
    "    def forward(self, x, lengths):\n",
    "        x = self.embed(x)\n",
    "        packed = pack_padded_sequence(x, lengths, batch_first=True) # 压缩掉填充值\n",
    "        out, _ = self.rnn(packed)\n",
    "        padded = pad_packed_sequence(out, batch_first=True) # 填充回来\n",
    "\n",
    "        # 双向RNN，隐藏层的最后一个维度的大小为2*embed_size，每个词的表示为(正向+后向)/ 2\n",
    "        # 注意：句子长度会减少到x中最长的句子的长度\n",
    "        cap_emb, _ = padded\n",
    "        cap_emb = (cap_emb[:,:,:cap_emb.size(2)//2] + cap_emb[:,:,cap_emb.size(2)//2:])/2\n",
    "\n",
    "        out = nn.functional.normalize(cap_emb, dim=-1)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### SCAN模型\n",
    "和VSE++模型一样，有了图像表示提取器和文本表示提取器，我们就可以构建SCAN模型了。\n",
    "- 这里同样需要注意要先按照文本的长短对数据进行排序，且为了评测模型时能够对齐图像和文本数据，我们需要恢复数据原始的输入顺序。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SCAN(nn.Module):\n",
    "    def __init__(self, vocab_size, word_dim, embed_size, num_layers, img_feat_dim):\n",
    "        super(SCAN, self).__init__()\n",
    "        self.image_encoder = ImageRepExtractor(img_feat_dim, embed_size)\n",
    "        self.text_encoder = TextRepExtractor(vocab_size, word_dim, embed_size, num_layers)\n",
    "        \n",
    "    def forward(self, images, captions, cap_lens):\n",
    "        # 按照caption的长短排序，并对照调整image的顺序\n",
    "        sorted_cap_lens, sorted_cap_indices = torch.sort(cap_lens, 0, True)\n",
    "        images = images[sorted_cap_indices]\n",
    "        captions = captions[sorted_cap_indices]\n",
    "        cap_lens = sorted_cap_lens\n",
    "\n",
    "        image_code = self.image_encoder(images)\n",
    "        text_code = self.text_encoder(captions, cap_lens)\n",
    "        if not self.training:\n",
    "            # 恢复数据原始的输入顺序\n",
    "            _, recover_indices = torch.sort(sorted_cap_indices)\n",
    "            image_code = image_code[recover_indices]\n",
    "            text_code = text_code[recover_indices]\n",
    "        return image_code, text_code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 定义损失函数\n",
    "和VSE++模型一样，SCAN模型也采用了在线挖掘困难样本的triplet损失函数。二者的不同之处在于图像和文本相关分数的计算方式。\n",
    "\n",
    "SCAN模型使用的是基于交叉注意力的相关性计算方法。\n",
    "\n",
    "- 首先利用交叉注意力计算图像或文本经过跨模态对齐之后的局部表示\n",
    "- 然后计算对齐前后局部表示之间的余弦相似度\n",
    "- 最后再使用累积函数综合局部相似度求得图文具体的匹配得分\n",
    "\n",
    "SCAN模型尝试了以图像为查询和以文本为查询的两种形式的交叉注意力，下面将首先介绍通用注意力函数的实现，然后分别介绍这两种形式的交叉注意力。本部分代码的实现参照了SCAN模型的作者发布的[源码](https://github.com/kuanghuei/SCAN)。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### 注意力函数\n",
    "\n",
    "不管是以图像为查询还是以文本为查询，我们首先都需要实现一个通用的计算注意力的函数。通用的注意力函数利用三个输入Q、K和V，输出可以理解为用V表示Q的结果。具体实现上，该函数包含三个步骤：\n",
    "- 选取Q和K的关联方式并计算注意力相关性分数；\n",
    "- 归一化Q和K的相关性分数；\n",
    "- 以相关性分数作为V的权重，计算输出。\n",
    "\n",
    "在跨模态注意力中，Q是一个模态，K和V是另一个模态。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def func_attention(query, key_value, smooth, func_attn_score='plain'):\n",
    "    \"\"\"\n",
    "    Q K V：Q和K算出相关性得分，作为V的权重，K=V\n",
    "    参数：\n",
    "        query: 查询 (batch_size, n_query, d)\n",
    "        key_value: 键和值，(batch_size, n_kv, d)\n",
    "    \"\"\"\n",
    "    batch_size, n_query = query.size(0), query.size(1)\n",
    "    n_kv = key_value.size(1)\n",
    "\n",
    "    # （2）计算query和key的相关性，实现a函数\n",
    "    # query^T: (batch_size, d, n_query)\n",
    "    queryT = torch.transpose(query, 1, 2)\n",
    "    # (batch_size, n_kv, d)(batch_size, d, n_query)\n",
    "    # => attn: (batch_size, n_kv, n_query)\n",
    "    attn = torch.bmm(key_value, queryT)\n",
    "    if func_attn_score == \"plain\":\n",
    "        pass\n",
    "    elif func_attn_score == \"softmax\":\n",
    "        attn = nn.Softmax(dim=2)(attn)\n",
    "    elif func_attn_score == \"l2norm\":\n",
    "        attn = nn.functional.normalize(attn, dim=2)\n",
    "    elif func_attn_score == \"clipped\":\n",
    "        attn = nn.LeakyReLU(0.1)(attn)\n",
    "    elif func_attn_score == \"clipped_l2norm\":\n",
    "        attn = nn.LeakyReLU(0.1)(attn)\n",
    "        attn = nn.functional.normalize(attn, dim=2)\n",
    "    else:\n",
    "        raise ValueError(\"unknown function for attention score:\", func_attn_score)\n",
    "    # （3）归一化相关性分数\n",
    "    # (batch_size, n_query, n_kv)\n",
    "    attn = torch.transpose(attn, 1, 2).contiguous()\n",
    "    # (batch_size*n_query, n_kv)\n",
    "    attn = attn.view(batch_size*n_query, n_kv)\n",
    "    attn = nn.Softmax(dim=1)(attn*smooth)\n",
    "    # (batch_size, n_query, n_kv)\n",
    "    attn = attn.view(batch_size, n_query, n_kv)\n",
    "    # (batch_size, n_kv, n_query)\n",
    "    attnT = torch.transpose(attn, 1, 2).contiguous()\n",
    "    # （4）计算输出\n",
    "    # (batch_size, d, n_kv)\n",
    "    key_valueT = torch.transpose(key_value, 1, 2)\n",
    "    # (batch_size x d x n_kv)(batch_size x n_kv x n_query)\n",
    "    # => (batch_size, d, n_query)\n",
    "    output = torch.bmm(key_valueT, attnT)\n",
    "    # --> (batch_size, n_query, d)\n",
    "    output = torch.transpose(output, 1, 2)\n",
    "\n",
    "    return output, attnT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### 交叉注意力 \n",
    "\n",
    "SCAN模型尝试了两种形式的交叉注意力：图像-文本交叉注意力和文本-图像注意力。\n",
    "- 图像-文本跨模态注意力是以图像模态为查询，文本模态为键和值的交叉注意力。\n",
    "- 文本-图像跨模态注意力是以文本模态为查询，图像模态为键和值的交叉注意力。\n",
    "\n",
    "对于交叉注意力计算过程中涉及的累积函数，SCAN模型也尝试了两种形式：\n",
    "- 平均值函数；\n",
    "- 可看作平滑max函数的LogSumExp函数。\n",
    "  - 这里实际使用的是带超参数$\\lambda_{lse}$的LogSumExp函数，即$\\rm log(\\sum_{i=1}^n e^{\\lambda_{lse}x_i})/\\lambda_{lse}$。$\\rm \\lambda_{lse}$可以控制函数的精确程度，显然，由于指数函数越到后面变化幅度越大，因此$\\rm \\lambda_{lse}$取值越大，LogSumExp越逼近max函数，$\\rm \\lambda_{lse}$取值越小，LogSumExp越平滑。\n",
    "\n",
    "下面的函数给出了交叉注意力的具体实现。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def xattn_score(images, captions, cap_lens, config):\n",
    "    \"\"\"\n",
    "    参数：\n",
    "        Images: (batch_size, n_regions, d) \n",
    "        Captions: (batch_size, max_n_words, d)\n",
    "        CapLens: (batch_size) array of caption lengths\n",
    "    \"\"\"\n",
    "    similarities = []\n",
    "    n_image = images.size(0)\n",
    "    n_caption = captions.size(0)\n",
    "    for i in range(n_caption):\n",
    "        # 获得第i条文本描述\n",
    "        n_word = cap_lens[i]\n",
    "        cap_i = captions[i, :n_word, :].unsqueeze(0).contiguous()\n",
    "        # 将第i条文本复制至n_image份 \n",
    "        # (n_image, n_word, d)\n",
    "        cap_i_expand = cap_i.repeat(n_image, 1, 1)\n",
    "        if config.cross_attn == 'i2t':\n",
    "            \"\"\"\n",
    "                图像-文本交叉注意力：用文本中的词来表示图像中的每个区域，所有图片的所有区域用当前文本表示\n",
    "                images(query): (n_image, n_region, d)\n",
    "                cap_i_expand(key_value): (n_image, n_word, d)\n",
    "                align_feature: (n_image, n_region, d)\n",
    "            \"\"\"\n",
    "            align_feature, _ = func_attention(images, cap_i_expand, config.lambda_softmax, config.func_attn_score)\n",
    "            # 当前文本和图片的相关度\n",
    "            # (n_image, n_region)\n",
    "            row_sim = nn.functional.cosine_similarity(images, align_feature, dim=2)\n",
    "        elif config.cross_attn == 't2i':\n",
    "            \"\"\"\n",
    "                文本-图像交叉注意力：用图像表示文本中的每个词，当前文本中的词用所有图片来表示\n",
    "                cap_i_expand(query): (n_image, n_word, d)\n",
    "                images(key_value): (n_image, n_regions, d)\n",
    "                align_feature: (n_image, n_word, d)\n",
    "            \"\"\"\n",
    "            align_feature, _ = func_attention(cap_i_expand, images, config.lambda_softmax, config.func_attn_score)\n",
    "            # 当前文本和图片的相关度\n",
    "            # (n_image, n_word)\n",
    "            row_sim = nn.functional.cosine_similarity(cap_i_expand, align_feature, dim=2)\n",
    "        else:\n",
    "            raise ValueError(\"unknown cross attention type: {}\".format(config.cross_attn))\n",
    "        # 累计函数\n",
    "        if config.agg_func == 'LSE':\n",
    "            row_sim = torch.logsumexp(row_sim.mul_(config.lambda_lse), dim=1, keepdim=True) / config.lambda_lse\n",
    "        elif config.agg_func == 'AVG':\n",
    "            row_sim = row_sim.mean(dim=1, keepdim=True)\n",
    "        else:\n",
    "            raise ValueError(\"unknown aggfunc: {}\".format(config.agg_func))\n",
    "        similarities.append(row_sim)\n",
    "\n",
    "    # (n_image, n_caption)\n",
    "    similarities = torch.cat(similarities, 1)\n",
    "    return similarities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 困难样本挖掘的triplet损失函数\n",
    "\n",
    "和VSE++模型中使用的损失函数的唯一区别在于图文关联分数的计算采用了交叉注意力方法。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class XAttnTripletNetLoss(nn.Module):\n",
    "    def __init__(self, config, margin=0.2, hard_negative=False):\n",
    "        super(XAttnTripletNetLoss, self).__init__()\n",
    "        self.config = config\n",
    "        self.margin = margin\n",
    "        self.hard_negative = hard_negative\n",
    "\n",
    "    def forward(self, ie, te, tl):\n",
    "        # 和vse++模型唯一的区别在于图文关联分数的计算\n",
    "        scores = xattn_score(ie, te, tl, self.config)\n",
    "        diagonal = scores.diag().view(ie.size(0), 1)\n",
    "        d1 = diagonal.expand_as(scores)\n",
    "        d2 = diagonal.t().expand_as(scores)\n",
    "\n",
    "        # 图像为锚\n",
    "        cost_i = (self.margin + scores - d1).clamp(min=0)\n",
    "        # 文本为锚\n",
    "        cost_t = (self.margin + scores - d2).clamp(min=0)\n",
    "\n",
    "        mask = torch.eye(scores.size(0), dtype=torch.bool)\n",
    "        I =  torch.autograd.Variable(mask)\n",
    "        if torch.cuda.is_available():\n",
    "            I = I.cuda()\n",
    "        cost_i = cost_i.masked_fill_(I, 0)\n",
    "        cost_t = cost_t.masked_fill_(I, 0)\n",
    "\n",
    "        # 找困难样本\n",
    "        if self.hard_negative:\n",
    "            cost_i = cost_i.max(1)[0]\n",
    "            cost_t = cost_t.max(0)[0]\n",
    "\n",
    "        return cost_i.sum() + cost_t.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 选择优化方法\n",
    "\n",
    "这里选用Adam优化算法来更新模型参数，学习速率采用分段衰减方法。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_optimizer(model, config):\n",
    "    return torch.optim.Adam(params=filter(lambda p: p.requires_grad, model.parameters()), lr=config.learning_rate)\n",
    "    \n",
    "def adjust_learning_rate(optimizer, epoch, config):\n",
    "    \"\"\"每隔lr_update个轮次，学习速率减小至当前十分之一\"\"\"\n",
    "    lr = config.learning_rate * (0.1 ** (epoch // config.lr_update))\n",
    "    for param_group in optimizer.param_groups:\n",
    "        param_group['lr'] = lr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 评估指标\n",
    "这里同样使用跨模态检索中最常用的评估指标Recall@K。\n",
    "\n",
    "- 首先利用SCAN模型计算图像和文本编码\n",
    "- 然后直接计算所有图像编码和所有文本编码之间的相似度得分\n",
    "- 最后利用得分排序计算Recall@K\n",
    "\n",
    "需要注意的是，这里的图文编码间的相似度得分是基于交叉注意力计算得到的，计算复杂度较高，需要在GPU上执行。然而，GPU无法一次性存储所有的测试数据，因此，我们需要分块计算相似度得分，具体实现细节见函数calc_score。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "def evaluate(data_loader, model, batch_size, config):\n",
    "    # 模型切换进入评估模式\n",
    "    model.eval()\n",
    "    device = next(model.parameters()).device\n",
    "    max_len = config.max_len + 2\n",
    "    image_codes = torch.zeros((len(data_loader.dataset), config.max_boxes, config.embed_size))\n",
    "    text_codes = torch.zeros((len(data_loader.dataset), max_len, config.embed_size))\n",
    "    cap_lens = []\n",
    "    \n",
    "    for i, (imgs, caps, caplens) in enumerate(data_loader):\n",
    "        with torch.no_grad():\n",
    "            image_code, text_code = model(imgs.to(device), caps.to(device), caplens)\n",
    "            # 将图文对应表示存到numpy数组中，gpu一般无法存储全部数据，因此先存储至系统内存中\n",
    "            image_codes[i*batch_size:(i+1)*batch_size] = image_code.data.cpu()\n",
    "            text_codes[i*batch_size:(i+1)*batch_size,:text_code.size(1),:] = text_code.data.cpu()\n",
    "            cap_lens.extend(caplens)\n",
    "    res = calc_recall(image_codes, text_codes, cap_lens, config)\n",
    "    # 模型切换回训练模式\n",
    "    model.train()\n",
    "    return res\n",
    "\n",
    "def calc_score(image_codes, text_codes, cap_lens, config):\n",
    "    # 分块计算图文相似度得分，这里，每次计算500张图片和1000条文本的相似度得分\n",
    "    image_bs = 500\n",
    "    text_bs = 1000\n",
    "    image_num = image_codes.size(0)\n",
    "    text_num = text_codes.size(0)\n",
    "    n_image_batch = math.ceil(image_num / float(image_bs))\n",
    "    n_text_batch = math.ceil(text_num / float(text_bs))\n",
    "    scores = []\n",
    "    for i in range(n_text_batch):\n",
    "        text_code = text_codes[i*text_bs:min((i+1)*text_bs,text_num)].cuda()\n",
    "        tmp_scores = []\n",
    "        for j in range(n_image_batch):\n",
    "            image_code = image_codes[j*image_bs:min((j+1)*image_bs,image_num)].cuda()\n",
    "            tmp_scores.append(xattn_score(image_code, text_code, cap_lens, config))\n",
    "        # n_text_batch个(image_bs, text_bs)矩阵块，拼接成矩阵(image_num, text_bs)\n",
    "        # 最终是在cpu上计算recall\n",
    "        scores.append(torch.cat(tmp_scores, 0).cpu())\n",
    "    scores = torch.cat(scores, 1).numpy()\n",
    "    return scores\n",
    "\n",
    "def calc_recall(image_codes, text_codes, cap_lens, config): \n",
    "    # 之所以可以每隔固定数量取图片，是因为前面对图文数据对输入顺序进行了还原\n",
    "    cpi = config.captions_per_image\n",
    "    image_codes = image_codes[::cpi]\n",
    "    scores = calc_score(image_codes, text_codes, cap_lens, config)\n",
    "     # 以图检文：按行从大到小排序\n",
    "    sorted_scores_indices = (-scores).argsort(axis=1)\n",
    "    (n_image, n_text) = scores.shape\n",
    "    ranks_i2t = np.zeros(n_image)\n",
    "    for i in range(n_image):\n",
    "         # 一张图片对应cpi条文本，找到排名最高前的文本位置\n",
    "        min_rank = 1e10\n",
    "        for j in range(i*cpi,(i+1)*cpi):\n",
    "            rank = list(sorted_scores_indices[i,:]).index(j)\n",
    "            if min_rank > rank:\n",
    "                min_rank = rank\n",
    "        ranks_i2t[i] = min_rank\n",
    "    # 以文检图：按列从大到小排序\n",
    "    sorted_scores_indices = (-scores).argsort(axis=0)\n",
    "    ranks_t2i = np.zeros(n_text)\n",
    "    for i in range(n_text):\n",
    "        rank = list(sorted_scores_indices[:,i]).index(i//cpi)\n",
    "        ranks_t2i[i] = rank\n",
    "    # 最靠前的位置小于k，即为recall@k\n",
    "    r1_i2t = 100.0 * len(np.where(ranks_i2t<1)[0]) / n_image\n",
    "    r1_t2i = 100.0 * len(np.where(ranks_t2i<1)[0]) / n_text\n",
    "    r5_i2t = 100.0 * len(np.where(ranks_i2t<5)[0]) / n_image\n",
    "    r5_t2i = 100.0 * len(np.where(ranks_t2i<5)[0]) / n_text\n",
    "    r10_i2t = 100.0 * len(np.where(ranks_i2t<10)[0]) / n_image\n",
    "    r10_t2i = 100.0 * len(np.where(ranks_t2i<10)[0]) / n_text\n",
    "    return r1_i2t, r1_t2i, r5_i2t, r5_t2i, r10_i2t, r10_t2i\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 训练模型\n",
    "\n",
    "和VSE++模型一样，训练模型过程还是分为读取数据、前馈计算、计算损失、更新参数、选择模型五个步骤。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = Namespace(\n",
    "    captions_per_image = 5,\n",
    "    max_len = 30,\n",
    "    max_boxes = 36,\n",
    "    batch_size = 128,\n",
    "    word_dim = 300,\n",
    "    embed_size = 1024,\n",
    "    num_layers = 1,\n",
    "    img_feat_dim = 2048,\n",
    "    learning_rate = 0.0005,\n",
    "    lr_update = 10,\n",
    "    margin = 0.2,\n",
    "    hard_negative = True,\n",
    "    num_epochs = 30,\n",
    "    grad_clip = 2,\n",
    "    evaluate_step = 60,\n",
    "    checkpoint = None,\n",
    "    best_checkpoint = '../model/scan/best_flickr8k.pth.tar',\n",
    "    last_checkpoint = '../model/scan/last_flickr8k.pth.tar',\n",
    "    func_attn_score = 'clipped_l2norm', # plain|softmax|clipped|l2norm|clipped_l2norm\n",
    "    agg_func = 'LSE', # LSE|AVG\n",
    "    cross_attn = 't2i', # t2i|i2t\n",
    "    lambda_lse = 6,\n",
    "    lambda_softmax = 9\n",
    ")\n",
    "\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '2'\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\") \n",
    "\n",
    "# 数据\n",
    "data_dir = '../data/flickr8k/'\n",
    "vocab_path = '../data/flickr8k/vocab.json'\n",
    "train_loader, valid_loader, test_loader = mktrainval(data_dir, \n",
    "                                                     vocab_path, \n",
    "                                                     config.batch_size)\n",
    "# 模型\n",
    "with open(vocab_path, 'r') as f:\n",
    "    vocab = json.load(f)\n",
    "model = SCAN(len(vocab), config.word_dim, config.embed_size, config.num_layers, config.img_feat_dim)\n",
    "model.to(device)\n",
    "model.train()\n",
    "\n",
    "# 损失函数\n",
    "loss_fn = XAttnTripletNetLoss(config, config.margin, True)\n",
    "\n",
    "# 优化器\n",
    "optimizer = get_optimizer(model, config)\n",
    "    \n",
    "start_epoch = 0\n",
    "best_res = 0\n",
    "print(\"开始训练！\")\n",
    "for epoch in range(start_epoch, config.num_epochs):\n",
    "    adjust_learning_rate(optimizer, epoch, config)\n",
    "    for i, (imgs, caps, caplens) in enumerate(train_loader):\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        imgs = imgs.to(device)\n",
    "        caps = caps.to(device)\n",
    "\n",
    "        image_code, text_code = model(imgs, caps, caplens)\n",
    "        loss = loss_fn(image_code, text_code, caplens)\n",
    "        loss.backward()\n",
    "        \n",
    "        # 梯度截断\n",
    "        if config.grad_clip > 0:\n",
    "            nn.utils.clip_grad_norm_(model.parameters(), config.grad_clip)\n",
    "\n",
    "        optimizer.step()\n",
    "\n",
    "        state = {\n",
    "            'epoch': epoch,\n",
    "            'step': i,\n",
    "            'model': model,\n",
    "            'optimizer': optimizer\n",
    "        }\n",
    "        \n",
    "        if (i+1) % config.evaluate_step == 0:\n",
    "            r1_i2t, r1_t2i, r5_i2t, r5_t2i, r10_i2t, r10_t2i = evaluate(valid_loader, model, config.batch_size, config)\n",
    "            recall_sum = r1_i2t + r1_t2i + r5_i2t + r5_t2i + r10_i2t + r10_t2i\n",
    "            if best_res < recall_sum:\n",
    "                best_res = recall_sum\n",
    "                torch.save(state, config.best_checkpoint)\n",
    "            torch.save(state, config.last_checkpoint)\n",
    "            print('''epoch: %d, step: %d, loss: %.2f, \n",
    "                  I2T R@1: %.2f, T2I R@1: %.2f,  \n",
    "                  I2T R@5: %.2f, T2I R@5: %.2f, \n",
    "                  I2T R@10: %.2f, T2I R@10: %.2f,''' % \n",
    "                  (epoch, i+1, loss.item(), r1_i2t, r1_t2i, r5_i2t, r5_t2i, r10_i2t, r10_t2i))\n",
    "            \n",
    "checkpoint = torch.load(config.best_checkpoint)\n",
    "model = checkpoint['model']\n",
    "r1_i2t, r1_t2i, r5_i2t, r5_t2i, r10_i2t, r10_t2i = evaluate(test_loader, model, config.batch_size, config)\n",
    "print(\"Evaluate on the test set with the model that has the best performance on the validation set\")\n",
    "print('Epoch: %d, \\\n",
    "      I2T R@1: %.2f, T2I R@1: %.2f, \\\n",
    "      I2T R@5: %.2f, T2I R@5: %.2f, \\\n",
    "      I2T R@10: %.2f, T2I R@10: %.2f' % \n",
    "      (checkpoint['epoch'], r1_i2t, r1_t2i, r5_i2t, r5_t2i, r10_i2t, r10_t2i))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "这段代码完成训练，最后一行会输出在验证集上表现最好的模型在测试集上的结果，具体如下：\n",
    "```\n",
    "Epoch: 26, I2T R@1: 41.60, T2I R@1: 29.56,  I2T R@5: 71.30, T2I R@5: 59.34, I2T R@10: 82.90, T2I R@10: 72.24\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "和之前实现的VSE++模型相比，SCAN模型在跨模态检索任务上的表现更优。在以图检文和以文检图任务上，\n",
    "- SCAN模型分别获得了41.6和29.56的Recall@1值；\n",
    "- VSE++模型获得的Recall@1值分别为22.20和17.30。"
   ]
  }
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